CRISP: Pre-LLM Yet Text-Driven Visual Token Pruning for Efficient LVLM Inference
Xu Li, Yi Zheng, Mengyang Zhao, Yuxuan Liang, Zhe Liu, Rui Zhu, Xiaolei Chen, Wei Zhou, Baoquan Zhao, Juncen Guo
Abstract
Large Vision-Language Models (LVLMs) typically require processing hundreds to thousands of visual tokens, leading to substantial inference overhead. Existing visual token pruning methods either operate before the LLM using text-agnostic heuristics or prune inside the LLM at the cost of efficiency and noisy cross-modal attention. To address these limitations, we propose CRISP, a pre-LLM yet text-driven visual token pruning framework that preserves both instruction-relevant evidence and essential scene context. CRISP works in a two-stage pipeline: Stage 1 first identifies text-aligned visual tokens, and Stage 2 enhances contextual completeness through semantic diversity. Extensive experiments on LLaVA-1.5 and LLaVA-NeXT demonstrate that CRISP achieves superior performance retention under aggressive pruning ratios, maintaining up to 99.5% accuracy while reducing inference cost and latency by more than 2 times. CRISP serves as a practical solution for efficient LVLM inference, especially in resource-constrained scenarios.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.